This is very good intro for quick understanding of the concept 👍
now that's some quantum technology, man... Being one of the beta testers of Stable Diffusion helps me understand this even more.
Learnt a lot about diffusion models, thanks for the video
you're so beautiful and explain the Diffusion model in the most simple way. as the chinese saying: 人狠话不多!:rocket-red-countdown-liftoff:
Very clear intro, thanks!
This was so helpful. Love this format of starting easier and add layers of explanations.
Such a great video to dive in! I'm live streaming learning about Diffusion, right now!
Great video for beginners! Really helpful, Thank you!
This video was awesome! Well done :) and thank you
Your explanation helped me a lot to better understand this interesting process. The only technical term I had to look up was: neural convolutional network. This technical term refers to a digital brain that is specially trained to recognize visual patterns. It is characterized by its ability to identify local features in images and process this information hierarchically. All in all, thank You for your explanation
Thanks for this video, this was very insightfull. Still have a lot to learn about this topic that will revolutionize our world so much
Thanks for clear explanations and link to the blog!
Great explanation of diffusion models. Thank you.
thanks for this great presentation
Thank you for your explanation!
❤🎉 amazing lecture
Thanks so much for a useful presentation…what a good idea to present in several levels!
Sorry, but I don't understand something very important. WHY would you add the noise and then substract the noise? Correct me if I'm wrong, but the rightmost noise image in this example is basically an encoded image of the original dog image, that can be decoded deterministically with the neural network, in multiple steps. That's nice and dandy. And I do understand that the noise image is not like a RAR archive, which, were it to be slightly modified, would just yield corruption errors, and instead the modified noise image would still generate... an image. NOW. 1. How do you get from the user text prompt to the noise image of what the user WANTED, that will THEN be denoised (decoded)? 2. How is it so that not every OTHER noise result from the text prompt (except previously deterministically encoded images like this dog image for example) will output just a bunch of garbled mess? And yes, I know that is sometimes the case, I used Stable Diffusion daily.
Now, that's a great explanation for Diffusion Models.
@pi5549